I use Deepgram as more than just a transcription tool; it's my gateway to turning raw audio into intelligence. I like how fast and real-time it is, which makes it really useful for live conversation, not just recordings. I also appreciate that it works well with my development setup and is easy to connect. The initial setup was fairly simple, and overall, it was easy to start using. We switched to Deepgram because it was faster and more accurate than the standard speech-to-text tool we used before. Review collected by and hosted on G2.com.

One thing that could be improved is accuracy with different accents or noisy audio. Also, the setup and fine-tuning can take a bit of time if you are new to it. Better handling of strong accents and background noise would make results more consistent across different real-world situations. Simplify the setup process. Review collected by and hosted on G2.com.

I like Deepgram's fast and highly accurate speech-to-text transcription. It processes audio in real time and handles different accents well, making the transcripts reliable. Real-time processing is useful for live conversations and meetings, as it provides instant transcriptions without waiting. Deepgram also makes it easy to build voice-enabled applications that can understand users clearly and respond quickly. The initial setup was very easy, with clear documentation, a simple API, and helpful examples that made integration smooth and quick. Using Deepgram allowed us to add voice transcription features without needing a large development team. Review collected by and hosted on G2.com.

Deepgram works very well overall, but I think it could improve in a few areas. For example, expanding support for more languages and improving accuracy in very noisy audio environments would make it even better. Enhancements in speaker detection and some advanced features could also improve the overall experience Review collected by and hosted on G2.com.

The best thing is end to end voice agent. In a modular manner to select different choice of models for STT, LLM, TTS Review collected by and hosted on G2.com.

Relatively high costing, lower concurrency limits, not easy option to ask for higher concurrency limit. Low control over text to text model (only prompting at best) Review collected by and hosted on G2.com.

Easy to set up and get first API response with helpful docs and UI. The Nova-3 AI model's error rate is low and split utterances with timestamps out-of-the-box is nice. Pricing is great and the free $200 credit to start is incredible. Review collected by and hosted on G2.com.

Although Nova-3 error rate is low, it does struggle compared to other ASR models, particularly with British accent. I will be keeping an eye on new model releases with lower WER. Review collected by and hosted on G2.com.

I love that Deepgram's API is super easy to set up in just a couple of minutes. I never even go into the UI because the API alone is great. The instant transcription is quick, reliable, and quite accurate, which is essential for my freight brokerage, where we need to call truckers all the time. The fact that it works better than our phone system provider is a big plus. I honestly don't spend any time on it, which is the biggest compliment I could give since it just works. Also, the setup process couldn't be easier—I'd give it a nine out of ten. All I had to do was plug the API key into Claude Code and the whole thing set itself up. Their documentation is really good too. Review collected by and hosted on G2.com.

Honestly no complaints Review collected by and hosted on G2.com.

I appreciate Deepgram as a developer-first platform and use it for streaming audio. Deepgram solves the real-world audio problem and connects with less than 300 milliseconds of latency, making interactions feel like real human talk. What I love most is that Deepgram actually gets how people talk in the real world. The best part of Deepgram is its insane speed, which is the difference between a clunky tool and a natural conversation. The initial setup is generally very straightforward and developer-friendly, especially for their SDK libraries for Node.js, Python, and Go, as well as their documentation and authentication. Review collected by and hosted on G2.com.

TTS language gap, pricing complexity, limited voice styles Review collected by and hosted on G2.com.

I find Deepgram works reliably, and the latency is quite low, which gives the feel of live functionality when transcribing speech. The live transcription feature is really fast, which I find great. For English, Deepgram performs very well and is mostly good for other languages too. Especially for Russian, the quality is superior compared to other tools. I appreciate that Deepgram's cloud is much faster than local alternatives like Whisper, and it's really GDPR compliant, offering EU servers which is a game changer for privacy. The initial setup was actually very easy due to clear documentation and a nice playground to try things before implementation. Review collected by and hosted on G2.com.

I tried using medical transcription, but it only works in English. For my work, it would be better and required to be in German. There's a special model designed specifically for medical language, mainly in English, and I would like to have it in German as well and in other languages also. Review collected by and hosted on G2.com.

I really like the Deepgram Flux model, especially its natural pause detection, which allows my AI agent to transcribe without sending an end event until I'm actually done talking. This feature gives it a more natural conversational ability rather than interrupting me while I'm still talking. I also like that Deepgram is really fast and really cheap. Review collected by and hosted on G2.com.

I mean, I guess it was kinda tricky to do the integration to make it super smooth with my agent, but I got it done, and it works well. Well, I'm using Cartesia for text to speech. And I was hoping to just use Deepgram Text to Speech, but I found that Cartesia has more natural voices. It was really easy except it needed a lot of tinkering to get the word timing working correctly. Review collected by and hosted on G2.com.

I think the SDK and WebSocket API are clean, which made setting up the streaming connection pretty straightforward. Running two separate WebSocket streams for my microphone and system audio worked well, and the connection was stable. The JSON message format also made sense, and the reconnection logic was easy to implement. From a developer experience standpoint, getting the basics up and running felt smooth. Nova three looked really promising with its impressive accuracy benchmarks and good latency numbers. Features like smart formatting and punctuation worked well out of the box. Deepgram has solid technology, and Nova three is a capable model with a good API design. Review collected by and hosted on G2.com.

The first big challenge was actually turn detection. Deepgram fires these utterance end events based on silence and the problem is it fires on every pause, not once per complete thought. So, in a real meeting, when someone is talking and they just pause for a breath or to think for half a second, it results in separate fragments. I spent a lot of time trying to tune utterance and milliseconds. It always felt like I was fighting the system instead of working with it. The second issue was real world accuracy. When I compared what Deepgram transcribed to what Loom captured, the differences were rough. Domain-specific words were getting hallucinated into completely wrong things, like 'contractor role' turning into 'podcast show.' And the key term prompting feature in Nova three, which could have helped, felt static, and I never got it properly integrated during development. The feature could be more accessible or documented better. The third thing was transcript mutability. Deepgram's streaming results are mutable, which makes sense technically, but for a real-time UI, it creates a flickering effect where text keeps changing. This made the experience feel unreliable from the user's perspective. Review collected by and hosted on G2.com.

I find Deepgram's UI to be intuitive, simple, and highly functional, making it easy for users to navigate and complete their tasks. The variety of models like Nova 2, 3, and Whisper allows me to fine-tune my usage based on language needs, which is very helpful for my Oral History Project. The sub-features within the model, such as diarization of speakers, redaction, sentiment analysis, and grammar, make it an excellent tool for working with ASR-related tasks. The fast transcription rates significantly improve time management by transcribing hours of audio in seconds, streamlining my workflow and enabling more interviews. Moreover, Deepgram's API is straightforward to integrate, creating an effortless experience for developing independent tools. The quality of transcriptions is greatly enhanced, and the ability to catalog based on efficiency, success rate, and accuracy is invaluable. Overall, Deepgram is not only reliable and cost-effective but is also a de facto choice for speech-to-text models. It's clearly a 10 for me, and I've already recommended it to colleagues because of the ongoing improvements and planned developments. Review collected by and hosted on G2.com.

The only issue is the inability to rename speakers and the absence of timestamps. I understand JSON files could solve this but it would save a lot of time. It takes me a while to extract all the details like timestamps and speaker changes from a JSON file. Furthermore, processing the word file results in a very large file size. Review collected by and hosted on G2.com.